Sugarcane transportation process modeling by time series approach

Document Type: Full Article


1 1Department of Agricultural Machinery and Mechanization Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz, Iran, I. R. Iran

2 Department of Agricultural Economic, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz, Iran, I. R. Iran


Sugarcane is one of the severely perishable crops that is used as raw material for white sugar production. Sucrose content of the sugarcane which is of high commercial value decreases in quality due to pre-harvest burning, high ambient temperature, kill-to-mill delays as well as microbial contaminations. Delays in sugarcane transportation are the most important risks which can affect the quality and quantity of the product. Delay in milling of the harvested sugarcane is caused by various reasons in agro-industry units including factory downtime, breakdowns of tractors in the waiting line at factory, tractor accident in factory yard and staff shift changes creating long queues. In order to reduce delays, the present study attempted to forecast arrival and service level of tractor drawn carts which transfer burned or cut canes from farm to mill. The univariate ARMA models were applied to forecast arrival and service level. The RMSE and MAPE were also used to evaluate precision of our forecast. The results of models demonstrated that ARMA(4,3) and ARMA(4,2) models are suitable for arrival and service level of tractor drawn carts, respectively. The predicted values trend of arrival, and service level truly reflected the actual values of arrival and service level as well as queue system tendency. The values of MSE, RMSE and MAPE that indicate accuracy of the forecasted carts arrival and service level were relatively low. The estimated models can be used to forecast values of arrival and service levels of tractor drawn carts for subsequent hours during harvest season.


Article Title [Persian]

مدل‌سازی فرآیند حمل نیشکر با استفاده از دیدگاه سری‌های زمانی

Authors [Persian]

  • فاطمه افشارنیا 1
  • افشین مرزبان 1
  • عباس عبد شاهی 2
1 گروه مهندسی ماشین‌های کشاورزی و مکانیزاسیون، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، اهواز، ج. ا. ایران
2 گروه اقتصاد کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، اهواز، ج. ا. ایران.
Abstract [Persian]

نیشکر یکی از محصولات به شدت فسادپذیر است که به صورت مواد خام برای تولید شکر سفید بکار می­رود. این منبع تجاری ساکارز بدلیل سوزاندن قبل از برداشت، دمای بالای محیط، تأخیر در آسیاب و همین­طور آلودگی­های میکروبی به سرعت دچار افت کیفیت می­شود. یکی از مهم‌ترین ریسک­های حمل و نقل نیشکر تأخیراتی است که در این فرآیند می­تواند کیفیت و کمیت محصول را تحت تأثیر قرار دهد. تأخیر در آسیاب کردن نیشکر برداشت شده به دلایل مختلفی از جمله خرابی کارخانه، خرابی تراکتورهای در صف، تصادف تراکتورها در محیط کارخانه و تغییر شیفت در کشت و صنعت­ها بوجود می­آید که سبب ایجاد صفی طولانی می­گردد. از این­رو، در این پژوهش تلاش گردید به پیش­بینی سیستم صف تحویل محصول نیشکر به کارخانه تولید شکر با استفاده از سری­های زمانی پرداخته شود تا زمینه­های بهسازی آن فراهم گردد. مدل ARMA جهت پیش­بینی نرخ ورود و نرخ سرویس تراکتورهای حمل نیشکر بکار گرفته شد و شاخص­های RMSE و MAPE جهت ارزیابی دقت پیش­بینی استفاده شدند. نتایج برازش مدل­ها نشان داد که به ترتیب مدل­های ARMA(4,3) و ARMA(4,2) برای نرخ ورود و نرخ سرویس تراکتورهای حمل نی مناسب بودند. روند مقادیر پیش­بینی­شده نرخ ورود و نرخ سرویس به خوبی بر مقادیر واقعی منطبق بود. با کاربرد این مدل­های توسعه یافته، مقادیر پیش­بینی شده را می­توان برای بقیه فصل برداشت نیشکر بکار برد و از تأخیرات بوجود آمده که موجب ضایع شدن مقادیر زیادی از محصول می­شود کاست.

Keywords [Persian]

  • ARMA
  • تأخیر در آسیاب کردن
  • نیشکر
  • حمل و نقل
Afsharnia, F., Asoodar, M. A., Abdeshahi, A., & Marzban, A. (2013). Failure rate analysis of four agricultural tractor models in southern Iran. Agricultural Engineering International: CIGR Journal, 15(4), 160- 170.
Afsharnia, F., & Marzban, A. (2017). The effect of usage and storing conditions on John Deere 3140 tractor failures in Khuzestan province, Iran. Journal of Biosystems Engineering, 42(2), 75-79.
Afsharnia, F., & Marzban, A. (2019). The risk analysis of sugarcane stem transportation operations delays using the FMEA-ANP hybrid approach. Journal of Agricultural Machinery, 9(2), 481-496.
Iranian Sugarcane and Byproduct Research and Training Institute (ISCRTI). (2017). Sugar cane factories laboratory guidelines. Khuzestan:Sugarcane Development Company and Subsidiary Industries annals Arifin, M. Z., Probowati, B. D., & Hastuti, S. (2015). Applications of Queuing Theory in the Tobacco Supply. Agriculture and Agricultural Science Procedia, 3, 255-261.
Box, G. E., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control San Francisco. California: Holden-Day.
Chatfield, C. (2000). Time-series forecasting (1st ed). UK: Chapman and Hall/CRC.
Chunhawong, K., Chaisan, T., Rungmekarat, S. & Khotavivattana, S. (2018). Sugar industry and utilization of its by-products in Thailand: an overview. Sugar Tech, 20(2), 111-115.
Clarke, S. J. (1991). Losses associated with cane yard operations and cane washing. In Proceedings of the annual congress-South African Sugar Technologists' Association, 95, 131-144.
 Fan, Q., & Fan, H. (2015). Reliability analysis and failure prediction of construction equipment with time series models. Journal of Advanced Management Science, 3(3), 203-210.
Farjam, A., Omid, M., Akram, A. & Fazel, Niari. Z. (2014). A neural network based modeling and sensitivity analysis of energy inputs for predicting seed and grain corn yields. Journal of Agricultural Science and Technology, 16, 767-778.
Gujarati, N. & Damodar. (2003). Basic econometric. New Delhi: McGraw-Hill.
Jadhav, V., Chinappa Reddy, B. V., Gaddi, G. M., & Kiresur, V. R. (2017). Exploration of different functional forms of growth models: a censorious analysis with reference to horticultural sector in Karanataka. International Journal of Tropical Agriculture, 34(4), 1107-1116.
Le Gal, P.Y., Lyne, P.W., Meyer, E., & Soler, L.G. (2008). Impact of sugarcane supply scheduling on mill sugar production: A South African case study. Agricultural systems, 96(1-3), 64-74.
Makridakis, S., & Hibbon, M. (1979). Accuracy of forecasting: An empirical investigation. Journal of the Royal Statistical Society. Series A (General), 142 ( 2), 97-145.
Noroozi, S., Asoodar, M. A., Marzban, A., & Moradi Telavat, M. R. (2015). Sensitivity comparison of the sugarcane mill delay in Iran. Green sugar cane is more sensitive or burned Elixir Agriculture, 85, 34378-34385.
Safa. M., Samarasinghe, S., & Nejat, M. (2015). Prediction of wheat production using artificial neural networks and investigating indirect factors affecting it: case study in Canterbury province, New Zealand. Journal of Agricultural Science and Technology, 17, 791-803.
Samarasinghe, S. (2007). Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition. Boca Raton: Auerbach, FL.
Sayed, G. E. (1972). Changes in the sugar components of cane during growth and processing, Ph. D. Thesis, College of Agriculture Library, University of Assiut.
Solomon, S. (2000). Post-harvest cane deterioration and its milling consequences. Sugar Tech, 2(1&2), 1-18.
Suresh, K. K., & Priya, S. K. (2011). Forecasting sugarcane yield of Tamilnadu using ARIMA models, Sugar Tech, 13(1), 23-26.
Thiesson, B., Chickering, D. M., Heckerman, D., & Meek, C. (2004). ARMA time-series modeling with graphical models. In Proceedings of the 20th conference on Uncertainty in artificial intelligence, AUAI Press, 552-560.